Group area search: A novel nature-inspired optimization algorithm
Changjun Liu, Yingni Zhai, Lichen Shi, Yixing Gao, Junhu Wei · 2013
A novel optimization algorithm, Group Area Search (GAS), is proposed, which is inspired by searching behavior patterns of human beings and social animals. In GAS, the search area of each individual is automatically adjusted and gradually shrunk to the most promising region. A cruising-following mechanism is introduced to GAS, which allows individuals with low fitness chances to follow the historical best individual. The algorithm strikes a good balance between global search and local search. The experimental results on 6 benchmark functions show that GAS has good performance on both unimodal and multimodal test functions, especially on multimodal ones. It significantly outperforms six other population-based algorithms. It shows potential to solve complicated function optimization problems.